Modeling railway disruption lengths with Copula Bayesian Networks |
| |
Affiliation: | 1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China;2. Section Transport Engineering and Logistics, Delft University of Technology, Delft, The Netherlands |
| |
Abstract: | Decreasing the uncertainty in the lengths of railway disruptions is a major help to disruption management. To assist the Dutch Operational Control Center Rail (OCCR) during disruptions, we propose the Copula Bayesian Network method to construct a disruption length prediction model. Computational efficiency and fast inference features make the method attractive for the OCCR’s real-time decision making environment. The method considers the factors influencing the length of a disruption and models the dependence between them to produce a prediction. As an illustration, a model for track circuit (TC) disruptions in the Dutch railway network is presented in this paper. Factors influencing the TC disruption length are considered and a disruption length model is constructed. We show that the resulting model’s prediction power is sound and discuss its real-life use and challenges to be tackled in practice. |
| |
Keywords: | Railway disruption Prediction Dependence model |
本文献已被 ScienceDirect 等数据库收录! |
|